Andreas Dittberner1,2, Erik Rodner3, Wolfgang Ortmann3, Joachim Stadler1,4, Carsten Schmidt5, Iver Petersen6, Andreas Stallmach5, Joachim Denzler3, Orlando Guntinas-Lichius1. 1. Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany. 2. Department of Otorhinolaryngology, Head and Neck Surgery, University of Erlangen-Nuremberg, Erlangen, Germany. 3. Department of Computer Science, Friedrich Schiller University, Jena, Germany. 4. Department of Otorhinolaryngology, Heinrich-Braun-Klinikum, Zwickau, Germany. 5. Division of Gastroenterology, Hepatology and Infectious Diseases, Department of Internal Medicine IV, Jena University Hospital, Jena, Germany. 6. Institute of Pathology, Jena University Hospital, Jena, Germany.
Abstract
BACKGROUND: The purpose of this study was to develop an automated image analysis algorithm to discriminate between head and neck cancer and nonneoplastic epithelium in confocal laser endomicroscopy (CLE) images. METHODS: CLE was applied to image head and neck cancer epithelium in vivo. Histopathologic diagnosis from biopsies was used to classify the CLE images offline as cancer or noncancer tissue. The classified images were used to train automated software based on distance map histograms. The performance of the final algorithm was confirmed by "leave 2 patients out" cross-validation and area under the curve (AUC)/receiver operating characteristic (ROC) analysis. RESULTS: Ninety-two CLE videos and 92 biopsies were analyzed from 12 patients. One hundred two frames of classified neoplastic tissue and 52 frames of nonneoplastic tissue were used for cross-validation of the developed algorithm. AUC varied from 0.52 to 0.92. CONCLUSION: The proposed software allows an objective classification of CLE images of head and neck cancer and adjacent nonneoplastic epithelium.
BACKGROUND: The purpose of this study was to develop an automated image analysis algorithm to discriminate between head and neck cancer and nonneoplastic epithelium in confocal laser endomicroscopy (CLE) images. METHODS: CLE was applied to image head and neck cancer epithelium in vivo. Histopathologic diagnosis from biopsies was used to classify the CLE images offline as cancer or noncancer tissue. The classified images were used to train automated software based on distance map histograms. The performance of the final algorithm was confirmed by "leave 2 patients out" cross-validation and area under the curve (AUC)/receiver operating characteristic (ROC) analysis. RESULTS: Ninety-two CLE videos and 92 biopsies were analyzed from 12 patients. One hundred two frames of classified neoplastic tissue and 52 frames of nonneoplastic tissue were used for cross-validation of the developed algorithm. AUC varied from 0.52 to 0.92. CONCLUSION: The proposed software allows an objective classification of CLE images of head and neck cancer and adjacent nonneoplastic epithelium.
Authors: Marc Aubreville; Christian Knipfer; Nicolai Oetter; Christian Jaremenko; Erik Rodner; Joachim Denzler; Christopher Bohr; Helmut Neumann; Florian Stelzle; Andreas Maier Journal: Sci Rep Date: 2017-09-20 Impact factor: 4.379
Authors: M Goncalves; M Aubreville; S K Mueller; M Sievert; A Maier; H Iro; C Bohr Journal: Acta Otorhinolaryngol Ital Date: 2019-01-31 Impact factor: 2.124
Authors: Andreas Ziebart; Denis Stadniczuk; Veronika Roos; Miriam Ratliff; Andreas von Deimling; Daniel Hänggi; Frederik Enders Journal: Front Oncol Date: 2021-05-11 Impact factor: 6.244